Browsing by Author "Wakefield, Brandon Jason"

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    Applying dynamic Bayesian Networks to process monitoring
    (Stellenbosch : Stellenbosch University, 2018-12) Wakefield, Brandon Jason; Auret, Lidia; Kroon, R. S. (Steve); Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.
    ENGLISH ABSTRACT: In efforts to reduce the impact of human error on the operation of chemical and mineral processing plants, reliable process monitoring solutions attempt to assist plant operators and engineers to detect and diagnose process faults before significant loss is incurred. An existing solution, the traditional multivariate statistical process monitoring (MSPM) approach, is able to reliably detect abnormal process behaviour but struggles to unambiguously identify the root cause of the abnormal behaviour. It was identified that this is caused by a lack of incorporation of existing process knowledge into the framework of the MSPM approach. It was proposed to investigate a different fault diagnosis approach which directly incorporates process knowledge into its framework. Lerner et al. (2000) and Lerner (2002) present such an approach, using probabilistic methods to infer process behaviour given a particular process model. This model is in the form of a dynamic Bayesian network (DBN), and would contain various models which each describe particular process behaviour given information about the operational status of various process components. In particular, these DBN models were able to describe normal process behaviour in addition to highly specific abnormal process behaviour caused by, for instance, a sensor fault or a blocked pipe. Using optimised methods, the authors could then use a DBN model to make predictions about process behaviour and infer, given observation of actual process behaviour, which combination of component statuses best describe that observation. Therefore, solving the fault diagnosis problem could be reduced to performing inference in a DBN using this approach. A probabilistic fault diagnosis (PD) approach based on Lerner et al. (2000) and Lerner (2002) was therefore implemented and investigated in this thesis. A survey of recent DBN-based PD approaches was also performed, and it was determined that relatively little research had been done on the topic. Furthermore, published results presenting fault diagnosis performance for DBN-based PD approaches were typically found to be useless for meaningful comparison with a traditional MSPM approach. In this regard, this thesis aimed to investigate the usefulness of the PD approach in comparison to the MSPM approach, while providing useful fault diagnosis performance metrics to facilitate comparison with other fault diagnosis approaches. The PD approach tested in this research also extended upon Lerner et al. (2000) and Lerner (2002) by including models for regulatory control systems and recycle streams based on the work by Yu and Rashid (2013). Additionally, from the same paper, the concept of abnormality likelihood index (ALI) was implemented in the PD approach. This enabled the PD approach to function more similarly to the MSPM approach, facilitating direct comparison. Generally, it was found that the PD approach could provide competitive fault detection when compared with the MSPM approach. However, this was at the cost of real-time fault detection as well as longer detection delay for incipient faults. On the other hand, it was found that the PD approach performed better at root cause analysis than the MSPM approach. In particular, the PD approach typically provided better isolation for the root cause of fault conditions. Despite some issues, similar results were observed for the PD approach when scaling up to larger processes. Nonetheless, these issues may be addressed with additional research, further improving the capabilities of the PD approach. Therefore, it was concluded that the PD approach is useful for fault diagnosis and should be investigated further in future research.